Overview

Dataset statistics

Number of variables17
Number of observations1176
Missing cells0
Missing cells (%)0.0%
Duplicate rows588
Duplicate rows (%)50.0%
Total size in memory156.3 KiB
Average record size in memory136.1 B

Variable types

Numeric10
Categorical7

Alerts

Dataset has 588 (50.0%) duplicate rowsDuplicates
DAY is highly overall correlated with DAWN and 9 other fieldsHigh correlation
SQUARE_UBICAZIONI is highly overall correlated with NR_LINES and 2 other fieldsHigh correlation
NR_LINES is highly overall correlated with SQUARE_UBICAZIONI and 2 other fieldsHigh correlation
DAWN is highly overall correlated with DAY and 9 other fieldsHigh correlation
SUNSET is highly overall correlated with DAY and 7 other fieldsHigh correlation
DAYLENGHT is highly overall correlated with DAY and 7 other fieldsHigh correlation
minTemperature is highly overall correlated with DAY and 6 other fieldsHigh correlation
maxTemperature is highly overall correlated with DAY and 8 other fieldsHigh correlation
POWER is highly overall correlated with SQUARE_UBICAZIONI and 2 other fieldsHigh correlation
DAY_ID is highly overall correlated with DAY and 9 other fieldsHigh correlation
LUN is highly overall correlated with DAY and 5 other fieldsHigh correlation
MAR is highly overall correlated with DAY and 2 other fieldsHigh correlation
MER is highly overall correlated with maxTemperature and 1 other fieldsHigh correlation
GIO is highly overall correlated with DAY and 3 other fieldsHigh correlation
VEN is highly overall correlated with DAY and 6 other fieldsHigh correlation
HIGH_POW is highly overall correlated with SQUARE_UBICAZIONI and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-07-01 21:05:31.149673
Analysis finished2023-07-01 21:05:49.888654
Duration18.74 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

SQUAREID
Real number (ℝ)

Distinct28
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5242.7857
Minimum4850
Maximum5552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:50.332906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4850
5-th percentile4966
Q15084.75
median5203.5
Q35433.25
95-th percentile5551
Maximum5552
Range702
Interquartile range (IQR)348.5

Descriptive statistics

Standard deviation190.6444
Coefficient of variation (CV)0.036363188
Kurtosis-0.87491039
Mean5242.7857
Median Absolute Deviation (MAD)120
Skewness-0.095553643
Sum6165516
Variance36345.289
MonotonicityNot monotonic
2023-07-01T23:05:50.443451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4850 42
 
3.6%
4966 42
 
3.6%
5551 42
 
3.6%
5550 42
 
3.6%
5437 42
 
3.6%
5436 42
 
3.6%
5435 42
 
3.6%
5434 42
 
3.6%
5433 42
 
3.6%
5320 42
 
3.6%
Other values (18) 756
64.3%
ValueCountFrequency (%)
4850 42
3.6%
4966 42
3.6%
4967 42
3.6%
4968 42
3.6%
5082 42
3.6%
5083 42
3.6%
5084 42
3.6%
5085 42
3.6%
5086 42
3.6%
5199 42
3.6%
ValueCountFrequency (%)
5552 42
3.6%
5551 42
3.6%
5550 42
3.6%
5437 42
3.6%
5436 42
3.6%
5435 42
3.6%
5434 42
3.6%
5433 42
3.6%
5320 42
3.6%
5319 42
3.6%

DAY
Real number (ℝ)

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.761905
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:50.566077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median15
Q322
95-th percentile28
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.437673
Coefficient of variation (CV)0.53532065
Kurtosis-1.2165758
Mean15.761905
Median Absolute Deviation (MAD)7
Skewness-0.032402044
Sum18536
Variance71.194326
MonotonicityNot monotonic
2023-07-01T23:05:50.683997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 56
 
4.8%
18 56
 
4.8%
28 56
 
4.8%
27 56
 
4.8%
26 56
 
4.8%
25 56
 
4.8%
22 56
 
4.8%
21 56
 
4.8%
20 56
 
4.8%
19 56
 
4.8%
Other values (11) 616
52.4%
ValueCountFrequency (%)
1 56
4.8%
4 56
4.8%
5 56
4.8%
6 56
4.8%
7 56
4.8%
8 56
4.8%
11 56
4.8%
12 56
4.8%
13 56
4.8%
14 56
4.8%
ValueCountFrequency (%)
29 56
4.8%
28 56
4.8%
27 56
4.8%
26 56
4.8%
25 56
4.8%
22 56
4.8%
21 56
4.8%
20 56
4.8%
19 56
4.8%
18 56
4.8%

DAY_ID
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
4
280 
0
224 
1
224 
2
224 
3
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row0
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

Length

2023-07-01T23:05:50.808927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:51.034569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

Most occurring characters

ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 280
23.8%
0 224
19.0%
1 224
19.0%
2 224
19.0%
3 224
19.0%

LUN
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
952 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Length

2023-07-01T23:05:51.159539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:51.268888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

MAR
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
952 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Length

2023-07-01T23:05:51.362616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:51.487545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

MER
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
952 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Length

2023-07-01T23:05:51.581274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:51.691174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

GIO
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
952 
1
224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Length

2023-07-01T23:05:51.784951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:51.894302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 952
81.0%
1 224
 
19.0%

VEN
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0
896 
1
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

Length

2023-07-01T23:05:51.990684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:52.115652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

Most occurring characters

ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 896
76.2%
1 280
 
23.8%

SQUARE_UBICAZIONI
Real number (ℝ)

Distinct27
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432.25
Minimum63
Maximum1288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:52.205802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile108
Q1209.25
median396
Q3574
95-th percentile1198
Maximum1288
Range1225
Interquartile range (IQR)364.75

Descriptive statistics

Standard deviation289.71314
Coefficient of variation (CV)0.67024439
Kurtosis1.8578027
Mean432.25
Median Absolute Deviation (MAD)187.5
Skewness1.3969291
Sum508326
Variance83933.703
MonotonicityNot monotonic
2023-07-01T23:05:52.338773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
396 84
 
7.1%
285 42
 
3.6%
210 42
 
3.6%
370 42
 
3.6%
228 42
 
3.6%
63 42
 
3.6%
610 42
 
3.6%
443 42
 
3.6%
207 42
 
3.6%
161 42
 
3.6%
Other values (17) 714
60.7%
ValueCountFrequency (%)
63 42
3.6%
108 42
3.6%
133 42
3.6%
161 42
3.6%
200 42
3.6%
202 42
3.6%
207 42
3.6%
210 42
3.6%
228 42
3.6%
270 42
3.6%
ValueCountFrequency (%)
1288 42
3.6%
1198 42
3.6%
693 42
3.6%
685 42
3.6%
684 42
3.6%
663 42
3.6%
610 42
3.6%
562 42
3.6%
500 42
3.6%
443 42
3.6%

NR_LINES
Real number (ℝ)

Distinct8
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8928571
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:52.448123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2264369
Coefficient of variation (CV)0.57192875
Kurtosis0.28441458
Mean3.8928571
Median Absolute Deviation (MAD)1.5
Skewness0.89415737
Sum4578
Variance4.9570213
MonotonicityNot monotonic
2023-07-01T23:05:52.760549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 252
21.4%
4 210
17.9%
3 210
17.9%
5 168
14.3%
7 126
10.7%
1 126
10.7%
8 42
 
3.6%
10 42
 
3.6%
ValueCountFrequency (%)
1 126
10.7%
2 252
21.4%
3 210
17.9%
4 210
17.9%
5 168
14.3%
7 126
10.7%
8 42
 
3.6%
10 42
 
3.6%
ValueCountFrequency (%)
10 42
 
3.6%
8 42
 
3.6%
7 126
10.7%
5 168
14.3%
4 210
17.9%
3 210
17.9%
2 252
21.4%
1 126
10.7%

DAWN
Real number (ℝ)

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437.33333
Minimum417
Maximum455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:52.869898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum417
5-th percentile421
Q1427
median436
Q3446
95-th percentile454
Maximum455
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.598581
Coefficient of variation (CV)0.026521147
Kurtosis-1.2280397
Mean437.33333
Median Absolute Deviation (MAD)10
Skewness-0.066347269
Sum514304
Variance134.52709
MonotonicityNot monotonic
2023-07-01T23:05:52.979247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
417 56
 
4.8%
441 56
 
4.8%
454 56
 
4.8%
453 56
 
4.8%
451 56
 
4.8%
450 56
 
4.8%
446 56
 
4.8%
445 56
 
4.8%
443 56
 
4.8%
442 56
 
4.8%
Other values (11) 616
52.4%
ValueCountFrequency (%)
417 56
4.8%
421 56
4.8%
422 56
4.8%
424 56
4.8%
425 56
4.8%
427 56
4.8%
431 56
4.8%
432 56
4.8%
434 56
4.8%
435 56
4.8%
ValueCountFrequency (%)
455 56
4.8%
454 56
4.8%
453 56
4.8%
451 56
4.8%
450 56
4.8%
446 56
4.8%
445 56
4.8%
443 56
4.8%
442 56
4.8%
441 56
4.8%

SUNSET
Real number (ℝ)

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.7143
Minimum991
Maximum1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:53.104256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum991
5-th percentile992
Q1996
median1002
Q31010
95-th percentile1016
Maximum1020
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6012369
Coefficient of variation (CV)0.0085779539
Kurtosis-1.0585748
Mean1002.7143
Median Absolute Deviation (MAD)8
Skewness0.37085066
Sum1179192
Variance73.981277
MonotonicityNot monotonic
2023-07-01T23:05:53.213606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
992 112
 
9.5%
998 112
 
9.5%
1020 56
 
4.8%
1002 56
 
4.8%
993 56
 
4.8%
994 56
 
4.8%
996 56
 
4.8%
997 56
 
4.8%
999 56
 
4.8%
1003 56
 
4.8%
Other values (9) 504
42.9%
ValueCountFrequency (%)
991 56
4.8%
992 112
9.5%
993 56
4.8%
994 56
4.8%
996 56
4.8%
997 56
4.8%
998 112
9.5%
999 56
4.8%
1002 56
4.8%
1003 56
4.8%
ValueCountFrequency (%)
1020 56
4.8%
1016 56
4.8%
1014 56
4.8%
1013 56
4.8%
1012 56
4.8%
1010 56
4.8%
1007 56
4.8%
1006 56
4.8%
1004 56
4.8%
1003 56
4.8%

DAYLENGHT
Real number (ℝ)

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean564.95238
Minimum536
Maximum603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:53.338577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum536
5-th percentile538
Q1549
median565
Q3583
95-th percentile594
Maximum603
Range67
Interquartile range (IQR)34

Descriptive statistics

Standard deviation20.168594
Coefficient of variation (CV)0.035699636
Kurtosis-1.1675273
Mean564.95238
Median Absolute Deviation (MAD)18
Skewness0.20907347
Sum664384
Variance406.7722
MonotonicityNot monotonic
2023-07-01T23:05:53.463542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
603 56
 
4.8%
558 56
 
4.8%
538 56
 
4.8%
539 56
 
4.8%
541 56
 
4.8%
543 56
 
4.8%
549 56
 
4.8%
552 56
 
4.8%
554 56
 
4.8%
556 56
 
4.8%
Other values (11) 616
52.4%
ValueCountFrequency (%)
536 56
4.8%
538 56
4.8%
539 56
4.8%
541 56
4.8%
543 56
4.8%
549 56
4.8%
552 56
4.8%
554 56
4.8%
556 56
4.8%
558 56
4.8%
ValueCountFrequency (%)
603 56
4.8%
594 56
4.8%
592 56
4.8%
589 56
4.8%
586 56
4.8%
583 56
4.8%
575 56
4.8%
573 56
4.8%
570 56
4.8%
568 56
4.8%

minTemperature
Real number (ℝ)

Distinct31
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9071429
Minimum-5.3
Maximum11.7
Zeros0
Zeros (%)0.0%
Negative168
Negative (%)14.3%
Memory size9.3 KiB
2023-07-01T23:05:53.594305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.3
5-th percentile-2.6
Q11.725
median6.2
Q37.4
95-th percentile9.8
Maximum11.7
Range17
Interquartile range (IQR)5.675

Descriptive statistics

Standard deviation4.0106475
Coefficient of variation (CV)0.81730808
Kurtosis-0.44771304
Mean4.9071429
Median Absolute Deviation (MAD)2.3
Skewness-0.61504942
Sum5770.8
Variance16.085294
MonotonicityNot monotonic
2023-07-01T23:05:53.714714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 98
 
8.3%
0.9 84
 
7.1%
6.1 84
 
7.1%
2.4 84
 
7.1%
7.5 56
 
4.8%
9.8 56
 
4.8%
6.2 56
 
4.8%
6.9 56
 
4.8%
11.7 42
 
3.6%
-1.6 42
 
3.6%
Other values (21) 518
44.0%
ValueCountFrequency (%)
-5.3 14
 
1.2%
-4.2 14
 
1.2%
-3.7 14
 
1.2%
-2.6 42
3.6%
-1.6 42
3.6%
-0.3 42
3.6%
0.6 14
 
1.2%
0.9 84
7.1%
1.2 28
 
2.4%
1.9 14
 
1.2%
ValueCountFrequency (%)
11.7 42
3.6%
10.9 14
 
1.2%
9.8 56
4.8%
9.6 14
 
1.2%
9.1 42
3.6%
8.3 14
 
1.2%
7.9 42
3.6%
7.5 56
4.8%
7.4 42
3.6%
7.3 14
 
1.2%

maxTemperature
Real number (ℝ)

Distinct38
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.634524
Minimum4.9
Maximum17.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:53.857355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile5
Q17.9
median10.25
Q314
95-th percentile16
Maximum17.5
Range12.6
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation3.5865634
Coefficient of variation (CV)0.3372566
Kurtosis-1.1863949
Mean10.634524
Median Absolute Deviation (MAD)3.45
Skewness0.15325267
Sum12506.2
Variance12.863437
MonotonicityNot monotonic
2023-07-01T23:05:54.009802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
8 56
 
4.8%
13.8 56
 
4.8%
10.7 56
 
4.8%
16 42
 
3.6%
6.2 42
 
3.6%
9.8 42
 
3.6%
4.9 42
 
3.6%
6.7 42
 
3.6%
10.4 42
 
3.6%
6.9 42
 
3.6%
Other values (28) 714
60.7%
ValueCountFrequency (%)
4.9 42
3.6%
5 42
3.6%
5.7 14
 
1.2%
6.2 42
3.6%
6.4 14
 
1.2%
6.7 42
3.6%
6.9 42
3.6%
7.2 42
3.6%
7.6 14
 
1.2%
8 56
4.8%
ValueCountFrequency (%)
17.5 14
 
1.2%
17.4 14
 
1.2%
17 14
 
1.2%
16 42
3.6%
15.6 42
3.6%
15.4 42
3.6%
15 14
 
1.2%
14.8 14
 
1.2%
14.5 14
 
1.2%
14.4 42
3.6%

POWER
Real number (ℝ)

Distinct588
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.335954
Minimum2.1408803
Maximum221.14286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-07-01T23:05:54.173468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.1408803
5-th percentile4.3677751
Q113.538215
median26.934927
Q360.796008
95-th percentile158.11568
Maximum221.14286
Range219.00198
Interquartile range (IQR)47.257792

Descriptive statistics

Standard deviation47.221603
Coefficient of variation (CV)1.0415928
Kurtosis3.1316372
Mean45.335954
Median Absolute Deviation (MAD)18.901223
Skewness1.8216339
Sum53315.082
Variance2229.8798
MonotonicityNot monotonic
2023-07-01T23:05:54.323719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.17745171 2
 
0.2%
6.481649015 2
 
0.2%
42.85128681 2
 
0.2%
42.11281566 2
 
0.2%
43.27199179 2
 
0.2%
41.19175382 2
 
0.2%
43.06373095 2
 
0.2%
41.36158862 2
 
0.2%
41.27682655 2
 
0.2%
41.37406935 2
 
0.2%
Other values (578) 1156
98.3%
ValueCountFrequency (%)
2.140880339 2
0.2%
2.542200788 2
0.2%
2.583328834 2
0.2%
2.603454488 2
0.2%
2.631133938 2
0.2%
2.633360717 2
0.2%
2.64191742 2
0.2%
2.65293074 2
0.2%
2.687341679 2
0.2%
2.689705631 2
0.2%
ValueCountFrequency (%)
221.1428554 2
0.2%
220.6019208 2
0.2%
217.2954015 2
0.2%
216.5709743 2
0.2%
214.7460616 2
0.2%
214.6653608 2
0.2%
208.8816709 2
0.2%
208.8709601 2
0.2%
206.9694009 2
0.2%
206.2481219 2
0.2%

HIGH_POW
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
1
746 
0
430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Length

2023-07-01T23:05:54.453287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-01T23:05:54.562596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Most occurring characters

ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 746
63.4%
0 430
36.6%

Interactions

2023-07-01T23:05:47.969846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:34.434704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.989243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.375109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.773056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.224748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.057050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.567033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.959664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.565460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.106030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:34.677127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.138929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.524045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.909799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.424230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.223037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.708990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.111244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.708132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.238415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:34.818617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.276498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.660315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.038421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.581788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.361544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.850131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.247830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.845196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.375232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:34.956271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.412869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.796012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.307523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.770798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.522315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.983701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.390445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.982799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.499610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.091131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.549293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.926797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.429696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.957159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.693333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.118241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.696658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.113718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.643215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.240868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.691156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.075129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.558915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:41.152506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:42.858176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.260879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.840482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.257541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.785442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.385483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.828650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.211088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.697388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:41.323761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.000951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.403725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:45.977932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.399065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:48.920845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.532479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:36.971141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.355633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.838036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:41.497790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.142446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.546533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.126080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.546834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:49.061850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.684774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.113200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.505109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:39.974120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:41.685477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.289344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.690083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.283961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.694533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:49.195196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:35.839887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:37.251900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:38.640501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:40.106002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:41.858978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:43.436175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:44.819367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:46.428220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-07-01T23:05:47.835451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-07-01T23:05:54.703625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
SQUAREIDDAYSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERDAY_IDLUNMARMERGIOVENHIGH_POW
SQUAREID1.0000.000-0.203-0.3670.0000.0000.000-0.0080.030-0.0750.0000.0000.0000.0000.0000.0000.258
DAY0.0001.0000.0000.0001.000-0.999-1.000-0.825-0.8360.0580.5230.5220.5220.4120.5220.6170.000
SQUARE_UBICAZIONI-0.2030.0001.0000.7090.0000.0000.0000.012-0.0410.8780.0000.0000.0000.0000.0000.0000.778
NR_LINES-0.3670.0000.7091.0000.0000.0000.0000.008-0.0280.7650.0000.0000.0000.0000.0000.0000.743
DAWN0.0001.0000.0000.0001.000-0.999-1.000-0.825-0.8360.0580.5230.5220.5220.4120.5220.6170.000
SUNSET0.000-0.9990.0000.000-0.9991.0000.9990.8240.841-0.0580.5670.6730.4110.4110.4110.8150.000
DAYLENGHT0.000-1.0000.0000.000-1.0000.9991.0000.8250.836-0.0580.5240.6920.4420.3800.4420.5990.000
minTemperature-0.008-0.8250.0120.008-0.8250.8240.8251.0000.694-0.0440.4920.4520.3640.3870.6140.5920.000
maxTemperature0.030-0.836-0.041-0.028-0.8360.8410.8360.6941.000-0.0650.5330.6720.3350.5130.4070.6590.000
POWER-0.0750.0580.8780.7650.058-0.058-0.058-0.044-0.0651.0000.0000.0000.0000.0000.0240.0240.831
DAY_ID0.0000.5230.0000.0000.5230.5670.5240.4920.5330.0001.0000.9990.9990.9990.9990.9990.000
LUN0.0000.5220.0000.0000.5220.6730.6920.4520.6720.0000.9991.0000.2310.2310.2310.2670.000
MAR0.0000.5220.0000.0000.5220.4110.4420.3640.3350.0000.9990.2311.0000.2310.2310.2670.000
MER0.0000.4120.0000.0000.4120.4110.3800.3870.5130.0000.9990.2310.2311.0000.2310.2670.000
GIO0.0000.5220.0000.0000.5220.4110.4420.6140.4070.0240.9990.2310.2310.2311.0000.2670.000
VEN0.0000.6170.0000.0000.6170.8150.5990.5920.6590.0240.9990.2670.2670.2670.2671.0000.004
HIGH_POW0.2580.0000.7780.7430.0000.0000.0000.0000.0000.8310.0000.0000.0000.0000.0000.0041.000

Missing values

2023-07-01T23:05:49.392024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-01T23:05:49.677174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SQUAREIDDAYDAY_IDLUNMARMERGIOVENSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERHIGH_POW
0485014000012855417102060311.716.018.1774520
148504010000285542110165947.49.823.3387051
248505101000285542210145927.515.421.8743641
348506200100285542410135897.914.321.7737141
448507300010285542510125866.115.621.4267901
548508400001285542710105839.111.923.0649681
6485011010000285543110075759.813.924.2996711
7485012101000285543210065736.113.822.6148771
8485013200100285543410045706.614.423.4979161
9485014300010285543510035686.210.226.8310841
SQUAREIDDAYDAY_IDLUNMARMERGIOVENSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERHIGH_POW
116655521801000013314419995587.09.18.0405950
116755521910100013314429985567.08.08.3342630
116855522020010013314439985546.910.78.0821880
116955522130001013314459975520.98.28.2806960
117055522240000113314469965490.96.97.9933750
117155522501000013314509945432.410.47.7223360
117255522610100013314519935412.46.77.9234650
11735552272001001331453992539-0.34.98.0645730
11745552283000101331454992538-2.65.07.9274660
11755552294000011331455991536-1.66.27.2241810

Duplicate rows

Most frequently occurring

SQUAREIDDAYDAY_IDLUNMARMERGIOVENSQUARE_UBICAZIONINR_LINESDAWNSUNSETDAYLENGHTminTemperaturemaxTemperaturePOWERHIGH_POW# duplicates
0485014000012855417102060311.716.018.17745202
148504010000285542110165947.49.823.33870512
248505101000285542210145927.515.421.87436412
348506200100285542410135897.914.321.77371412
448507300010285542510125866.115.621.42679012
548508400001285542710105839.111.923.06496812
6485011010000285543110075759.813.924.29967112
7485012101000285543210065736.113.822.61487712
8485013200100285543410045706.614.423.49791612
9485014300010285543510035686.210.226.83108412